Abstract

Over the past decade, there have been substantial advances in knowledge representation (KR)techniques for AI planning. Typically, planners search a space of solution to find a suitableand most accurate sequence of actions to achieve a specific task from a set of initial and goalstates. However, the progress in this field still cannot cope with the ever increasing of thecomplexities of modern systems, which makes knowledge representation an expensive anderror prone process. Planning is considered as one of artificial intelligence fields whereknowledge representation (KR) is extremely critical. However, a little work has been aimedat “measuring” domain models; the aim of this research is to develop a set of criteria andmetrics to assess the accruing and complexity of a particular classical planning problemdomain model. To reach that point the system has to have enough knowledge and knowledgehas to be well represented for the problem at hand. In this report, we have outlined theprototype the system and design planning domain model metric tools.1.2 Introduction